Conventionally, split nitrogen (N) applications at tillering and stem elongation enhance winter wheat yield, protein content, and nitrogen use efficiency. Vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge index (NDRE), and leaf chlorophyll content (LCC) can be used as crop N status indicators (CNSIs) to easily underline the N deficiency. The aim of this study, conducted across 4 growing seasons in North-West Italy, was to create a model for regulating wheat fertilization rates and improve crop yield. The model relies on CNSIs measurements collected during the initial stages of stem elongation, aiming to achieve predetermined yield targets. In each year, the experimental design was a factorial combination of four N rates (0, 33, 66, and 99 kg N ha−1) at tillering and five at stem elongations (0, 33, 66, 99 and 132 kg N ha−1). The Aubusson cultivar, characterized by intermediate yield potential and protein content, was used to calibrate and validate the model in a 3-year trial (2018–2020), while the model was also applied to cv LG Ayrton (high yield potential) and Izalco (high protein content) in the 2020–21 season. Yield and protein content trends in function of N rate were parabolic or sigmoidal respectively and both tillering and stem elongation rate contributed to increase the grain yield and protein content. Furthermore, the significant interaction between tillering and stem elongation fertilization on grain yield suggested the possibility of correcting the N deficiency after tillering fertilization with a further application. A calibration function for a variable rate application was established related to the CNSIs; all of them were good predictors but NDRE showed a higher overall correlation (R2 = 0.479) with grain yield than NDVI (R2= 0.461) or the LCC values (R2= 0.236) considering all the 3 years of experiments. The model’s intercept was reduced according to the decrease in the grain yield goal. The model's validation was accomplished by comparing the outcomes predicted by the model yields with the measured. The yield’s Root Mean Square Error (RMSE) values were low for cv. Aubusson (0.85, on average) in all 3 years, while the RMSE was higher in 2021 for LG Ayrton (1.90) and Izalco (1.35), in a production situation with a higher yield potential. The results suggest that the topdressing N fertilization rate could be accurately determined from measured CNSI values for a site-specific N fertilization management, but they also highlight the requirement of a model adaptation for different genotypes and environments.

中文翻译：

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使用当季作物氮状态指标优化冬小麦的氮肥用量


传统上，在分蘖和茎伸长时施用分氮 （N） 可以提高冬小麦产量、蛋白质含量和氮利用效率。植被指数，如归一化差值植被指数 （NDVI）、归一化差值红边指数 （NDRE） 和叶绿素含量 （LCC），可以用作作物氮状态指标 （CNSI），以轻松强调氮缺乏。这项研究在意大利西北部的 4 个生长季节进行，目的是创建一个调节小麦施肥率和提高作物产量的模型。该模型依赖于在茎伸长初始阶段收集的 CNSI 测量值，旨在实现预定的产量目标。每年，实验设计是分蘖时 4 个 N 速率 （0、33、66 和 99 kg N ha-1） 和 茎伸长时 5 个 N 速率 （0、33、66、99 和 132 kg N ha-1） 的因子组合。以中等产量潜力和蛋白质含量为特征的 Aubusson 品种在为期 3 年的试验（2018-2020 年）中用于校准和验证该模型，同时该模型也应用于 cv LG Ayrton（高产量潜力）和 Izalco（高蛋白质含量）在 2020-21 赛季。产量和蛋白质含量在氮肥量函数中的趋势分别为抛物线形或乙状，分蘖率和茎伸长率均有助于提高籽粒产量和蛋白质含量。此外，分蘖施肥和茎伸长施肥对籽粒产量的显著交互作用表明，分蘖施肥后有可能通过进一步施肥来纠正氮缺乏。建立了与 CNSI 相关的可变速率应用的校准函数;所有这些都是很好的预测因子，但 NDRE 显示出更高的总体相关性 （R2 = 0.479） 的产量高于 NDVI （R2= 0.461） 或 LCC 值 （R2= 0.236），考虑到所有 3 年的实验。模型的截距根据谷物产量目标的减少而减少。该模型的验证是通过将模型产量预测的结果与测量的结果进行比较来完成的。cv. Aubusson 的产量均方根误差 （RMSE） 值在所有 3 年中都较低（平均为 0.85），而 LG Ayrton （1.90） 和 Izalco （1.35） 在 2021 年的 RMSE 较高，在产量潜力较高的生产情况下。结果表明，追肥氮施肥率可以从测量的 CNSI 值中准确确定，用于特定地点的氮肥管理，但它们也强调了模型适应不同基因型和环境的要求。